DHFP-PE: Dual-Precision Hybrid Floating Point Processing Element for AI Acceleration
- URL: http://arxiv.org/abs/2604.04507v2
- Date: Wed, 08 Apr 2026 22:31:38 GMT
- Title: DHFP-PE: Dual-Precision Hybrid Floating Point Processing Element for AI Acceleration
- Abstract summary: This paper presents a dual-precision floating-point MAC processing element supporting FP8 (E4M3, E5M2) and FP4 (2 x E2M1, 2 x E1M2) formats.<n>It achieves an operating frequency of 1.94 GHz with an area of 0.00396 mm2 and power consumption of 2.13 mW, resulting in up to 60.4% area reduction and 86.6% power savings.
- Score: 2.1512536685031285
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The rapid adoption of low-precision arithmetic in artificial intelligence and edge computing has created a strong demand for energy-efficient and flexible floating-point multiply-accumulate (MAC) units. This paper presents a dual-precision floating-point MAC processing element supporting FP8 (E4M3, E5M2) and FP4 (2 x E2M1, 2 x E1M2) formats, specifically optimized for low-power and high-throughput AI workloads. The proposed architecture employs a novel bit-partitioning technique that enables a single 4-bit unit multiplier to operate either as a standard 4 x 4 multiplier for FP8 or as two parallel 2 x 2 multipliers for 2-bit operands, achieving maximum hardware utilization without duplicating logic. Implemented in 28 nm technology, the proposed PE achieves an operating frequency of 1.94 GHz with an area of 0.00396 mm^2 and power consumption of 2.13 mW, resulting in up to 60.4% area reduction and 86.6% power savings compared to state-of-the-art designs, making it well suited for energy-constrained AI inference and mixed-precision computing applications when deployed within larger accelerator architectures.
Related papers
- SpikeMLLM: Spike-based Multimodal Large Language Models via Modality-Specific Temporal Scales and Temporal Compression [46.709828328948724]
Spiking Neural Networks (SNNs) offer inherent energy efficiency advantages on neuromorphic hardware.<n>We propose SpikeMLLM, the first spike-based framework for MLLMs.<n>We show that SpikeMLLM maintains near-lossless performance under aggressive timestep compression.
arXiv Detail & Related papers (2026-04-13T15:32:44Z) - BWTA: Accurate and Efficient Binarized Transformer by Algorithm-Hardware Co-design [71.97035034203275]
We analyze zero-point distortion in binarization and propose a Binary Weights & Ternary Activations (BWTA) quantization scheme.<n>For training, we propose Smooth Multi-Stage Quantization, combining a Levelwise Degradation Strategy and a MagnitudeAlignment Projection Factor.<n>Experiments show that BWTA approaches full-precision performance for Transformer-based models, with an average 3.5% drop on GLUE and less than 2% drop on five tasks.
arXiv Detail & Related papers (2026-04-05T04:25:07Z) - To Compress or Not? Pushing the Frontier of Lossless GenAI Model Weights Compression with Exponent Concentration [46.63567524455431]
Low-precision floating-point formats provide stability, memory savings, and hardware efficiency without dequantization overhead.<n>We propose Exponent-Concentrated FP8 (ECF8), a compression framework with entropy-aware encoding and GPU-optimized decoding.<n>Experiments on LLMs and DiTs up to 671B parameters demonstrate up to 26.9% memory savings and 177.1% throughput acceleration.
arXiv Detail & Related papers (2025-10-03T02:22:13Z) - Bridging the Gap Between Promise and Performance for Microscaling FP4 Quantization [77.67818998672516]
We present the first comprehensive study of MXFP4 and NVFP4 for post-training quantization.<n>We introduce Micro-Rotated-GPTQ (MR-GPTQ), a variant of the classic GPTQ quantization algorithm.<n>We show that MR-GPTQ matches or outperforms state-of-the-art accuracy.
arXiv Detail & Related papers (2025-09-27T09:22:21Z) - XR-NPE: High-Throughput Mixed-precision SIMD Neural Processing Engine for Extended Reality Perception Workloads [0.0]
XR-NPE is designed for extended reality (XR) perception like visual inertial odometry (VIO), object classification, and eye gaze extraction.<n>Results are released publicly, enabling designers and researchers to readily adopt and build upon them.
arXiv Detail & Related papers (2025-08-18T16:13:00Z) - OISMA: On-the-fly In-memory Stochastic Multiplication Architecture for Matrix-Multiplication Workloads [0.2796197251957244]
OISMA is a novel in-memory computing architecture that utilizes the computational simplicity of a quasi-stochastic computing domain (Bent-Pyramid system)<n>OISMA converts normal memory read operations into in-situ multiplication operations with a negligible cost.<n>The accuracy results show a significant decrease in the average relative Frobenius error, from 9.42% (for 4x4) to 1.81% (for 512x512)
arXiv Detail & Related papers (2025-08-12T10:24:33Z) - Pushing the Envelope of LLM Inference on AI-PC [45.081663877447816]
ultra-low-bit models (1/1.58/2-bit) match the perplexity and end-task performance of their full-precision counterparts using the same model size.<n>The computational efficiency of state-of-the-art inference runtimes (e.g. bitnet) used to deploy them remains underexplored.<n>We take a bottom-up approach: we first design and implement 1-bit and 2-bit microkernels optimized for modern CPUs, achieving peak computational efficiency.<n>We present end-to-end inference results with 2-bit models that outperform the current SOTA runtime bitnet
arXiv Detail & Related papers (2025-08-08T23:33:38Z) - VEXP: A Low-Cost RISC-V ISA Extension for Accelerated Softmax Computation in Transformers [13.984340807378457]
Accelerating Softmax is challenging due to its non-pointwise, non-linear nature, with exponentiation as the most demanding step.<n>We design a custom arithmetic block for Bfloat16 exponentiation leveraging a novel approximation algorithm based on Schraudolph's method.<n>We execute Softmax with 162.7$times$ less latency and 74.3$times$ less energy compared to the baseline cluster.
arXiv Detail & Related papers (2025-04-15T14:28:48Z) - An Inquiry into Datacenter TCO for LLM Inference with FP8 [18.01919466758935]
We analyze the computational characteristics of large language models (LLMs) inference from a TCO perspective.<n>We investigate key workload characteristics influencing TCO for AI accelerators from Intel (Gaudi 2 & 3) and NVIDIA (H100 & H200)<n>We find that Gaudi HPUs achieve superior utilization on thin GEMMs compared to their counterparts, especially in FP8-quantized models.
arXiv Detail & Related papers (2025-02-03T05:26:22Z) - Flex-PE: Flexible and SIMD Multi-Precision Processing Element for AI Workloads [0.0]
This work proposes a flexible and SIMD multiprecision processing element (FlexPE)<n>The proposed design achieves an improved throughput of up to 16X FxP4, 8X FxP8, 4X FxP16 and 1X FxP32 in pipeline mode with 100% time multiplexed hardware.
arXiv Detail & Related papers (2024-12-16T12:25:57Z) - QUIK: Towards End-to-End 4-Bit Inference on Generative Large Language
Models [57.04178959678024]
We show that the majority of inference computations for large generative models can be performed with both weights and activations being cast to 4 bits.
We achieve this via a hybrid quantization strategy called QUIK, which compresses most of the weights and activations to 4-bit.
We provide GPU kernels matching the QUIK format with highly-efficient layer-wise runtimes, which lead to practical end-to-end throughput improvements of up to 3.4x.
arXiv Detail & Related papers (2023-10-13T17:15:05Z) - DeepGEMM: Accelerated Ultra Low-Precision Inference on CPU Architectures
using Lookup Tables [49.965024476651706]
DeepGEMM is a lookup table based approach for the execution of ultra low-precision convolutional neural networks on SIMD hardware.
Our implementation outperforms corresponding 8-bit integer kernels by up to 1.74x on x86 platforms.
arXiv Detail & Related papers (2023-04-18T15:13:10Z) - RedMule: A Mixed-Precision Matrix-Matrix Operation Engine for Flexible
and Energy-Efficient On-Chip Linear Algebra and TinyML Training Acceleration [15.869673535117032]
Current training algorithms rely on floating-point matrix operations to meet the precision and dynamic range requirements.
RedMulE is a low-power specialized accelerator conceived for multi-precision floating-point General Matrix-Matrix Operations (GEMM-Ops) acceleration.
RedMulE achieves up to 58.5 GFLOPS and 117 GFLOPS for FP16 and FP8, respectively, with 99.4% utilization of the array of Computing Elements.
arXiv Detail & Related papers (2023-01-10T11:07:16Z) - Adaptable Butterfly Accelerator for Attention-based NNs via Hardware and
Algorithm Co-design [66.39546326221176]
Attention-based neural networks have become pervasive in many AI tasks.
The use of the attention mechanism and feed-forward network (FFN) demands excessive computational and memory resources.
This paper proposes a hardware-friendly variant that adopts a unified butterfly sparsity pattern to approximate both the attention mechanism and the FFNs.
arXiv Detail & Related papers (2022-09-20T09:28:26Z) - FP8 Formats for Deep Learning [49.54015320992368]
We propose an 8-bit floating point (FP8) binary interchange format consisting of two encodings.
E4M3's dynamic range is extended by not representing infinities and having only one mantissa bit-pattern for NaNs.
We demonstrate the efficacy of the FP8 format on a variety of image and language tasks, effectively matching the result quality achieved by 16-bit training sessions.
arXiv Detail & Related papers (2022-09-12T17:39:55Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.